For high-level autonomous driving, the development of an effective vehicle-to-vehicle (V2V) communication system is an utmost concern. Among several candidates, the millimeter-wave (mmWave) V2V communication system is considered as a promising one, as it is expected to provide high data rate. To overcome the high path loss at mmWave frequencies, the codebook-based beam-alignment approach has been widely studied as a means of generating narrow and strong beams. Despite its potential, many existing codebooks are designed to generate codewords with equal beamwidths, which may not be optimal for V2V communications. Our focus is on designing a beamforming codebook for V2V communications, taking into account the angular distribution characteristics of vehicles on the road. To achieve this, we propose an analytical model to represent the traffic density of vehicles on roads. Based on the proposed traffic density model, we derive the angular distribution of directional communication links between vehicles by considering realistic road structures, including straight roads, intersections, and roundabouts. Utilizing the angular distribution between vehicles, we propose a codebook design framework specifically tailored for V2V communication systems. Simulation results verify that our model fits well for realistic urban grid road environments, and the proposed codebook outperforms codebooks with equal beamwidths.

We develop a vehicle motion state estimation algorithm based on an orthogonal frequency division multiplexing radar system suitable for vehicle-to-infrastructure (V2I) communications scenarios. Due to the transverse movement with respect to the direction from the target to roadside unit (RSU), the measured velocity is distorted as the Doppler shift depends on the angle formed between the vehicle position and RSU. To compensate for the distorted velocity, the exact vehicle position is required. However, this cannot be obtained in a system with a single RSU. In this paper, we develop an efficient cooperative vehicle state estimation algorithm that reduces the number of RSUs required for localization compared to conventional schemes by eliminating unnecessary RSUs to overcome the degradation of target detection performance in low signal-to-noise ratio regimes. Furthermore, the singularity problem due to the absence of Doppler shift when the vehicle travels near the tangential direction can be addressed with spatially distributed RSUs. Numerical results verify that the proposed estimation algorithm outperforms the existing schemes in V2I scenarios.